Predictive Structure-based Guidelines for Identifying Optimal PEGylation Sites within Proteins and Understanding the Context-Dependence of Non-covalent Interaction Strength Our goal is to develop structure-based tools for identifying optimal PEGylation sites within peptides/proteins and to use these tools to enhance peptide/protein pharmacokinetic properties. Our central hypothesis is that optimal PEGylation sites should be characterized by the ability of the attached PEG to enhance peptide/protein conformational stability. Our rationale for this hypothesis is that unstable, unfolded or misfolded proteins tend to be non-functional and have more pharmacokinetic problems than folded proteins (i.e., are more aggregation- prone, more susceptible to proteolysis, and more readily recognized by antibodies). Therefore increases in protein conformational stability should also enhance protein pharmacokinetic properties However, current PEGylation efforts lack predictive tools for increasing protein stability; instead, a trial-and-error approach prevails, which frequently results in diminished biological activity relative to the non-PEGylated protein. Using our growing molecular-level understanding of PEG-based protein stabilization, we will develop predictive structure-based tools for generating PEGylated peptides/proteins with enhanced pharmacokinetic properties and undiminished function, thereby accelerating the development of better PEGylated protein drugs. We also seek to understand and predict how location, microenvironment, and structural context affect the strength of non-covalent interactions, including salt-bridges, cation-? interactions, and n to ?* interactions, among others.